This is an notebook containing code for generating plots for our talk at Cogsci 2018 (Madison, WI). The corresponding proceedings paper is here in PDF form, and here in .Rmd form.
Factor Analysis with confidence intervals using method = psych::fa(r = d_old_wide, nfactors = nfactors_old, n.iter = n_iter,
rotate = chosen_rot, cor = chosen_cor)
Factor Analysis using method = minres
Call: psych::fa(r = d_old_wide, nfactors = nfactors_old, n.iter = n_iter,
rotate = chosen_rot, cor = chosen_cor)
Standardized loadings (pattern matrix) based upon correlation matrix
factor1 factor2 factor3 h2 u2 com
hunger 0.98 -0.12 0.02 0.87 0.13 1.0
smell 0.77 -0.09 0.06 0.58 0.42 1.0
fear 0.77 0.23 -0.02 0.81 0.19 1.2
pain 0.72 0.18 -0.02 0.67 0.33 1.1
fatigue 0.50 0.24 0.25 0.62 0.38 1.9
nausea 0.48 -0.01 0.16 0.30 0.70 1.2
anger 0.42 0.41 0.14 0.62 0.38 2.2
guilt -0.11 0.80 -0.05 0.55 0.45 1.0
embarrassment -0.16 0.75 0.11 0.52 0.48 1.1
pride 0.16 0.69 0.02 0.63 0.37 1.1
hurt_feelings 0.06 0.68 0.03 0.52 0.48 1.0
sadness 0.20 0.65 0.03 0.60 0.40 1.2
love 0.32 0.50 -0.05 0.48 0.52 1.7
happiness 0.40 0.41 0.01 0.49 0.51 2.0
figuring_out 0.04 -0.10 0.74 0.53 0.47 1.0
choice 0.06 0.09 0.72 0.61 0.39 1.0
memory -0.17 0.05 0.71 0.46 0.54 1.1
temperature 0.04 -0.06 0.64 0.41 0.59 1.0
depth 0.03 -0.04 0.55 0.30 0.70 1.0
awareness 0.10 0.11 0.52 0.37 0.63 1.2
factor1 factor2 factor3
SS loadings 4.27 3.81 2.86
Proportion Var 0.21 0.19 0.14
Cumulative Var 0.21 0.40 0.55
Proportion Explained 0.39 0.35 0.26
Cumulative Proportion 0.39 0.74 1.00
With factor correlations of
factor1 factor2 factor3
factor1 1.00 0.49 0.36
factor2 0.49 1.00 0.29
factor3 0.36 0.29 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 12.3 with Chi Square of 1408.65
The degrees of freedom for the model are 133 and the objective function was 1.71
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 123 with the empirical chi square 81.59 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 192.49 with prob < 0.00057
Tucker Lewis Index of factoring reliability = 0.929
RMSEA index = 0.068 and the 90 % confidence intervals are 0.04 0.079
BIC = -447.53
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
factor1 factor2 factor3
Correlation of (regression) scores with factors 0.97 0.94 0.92
Multiple R square of scores with factors 0.95 0.89 0.85
Minimum correlation of possible factor scores 0.90 0.78 0.69
Coefficients and bootstrapped confidence intervals
low factor1 upper low factor2 upper low factor3 upper
hunger 0.83 0.98 1.05 -0.16 -0.12 0.04 -0.06 0.02 0.17
smell 0.61 0.77 0.89 -0.22 -0.09 0.09 -0.05 0.06 0.19
fear 0.68 0.77 0.95 0.10 0.23 0.37 -0.16 -0.02 0.07
pain 0.51 0.72 0.92 0.08 0.18 0.37 -0.10 -0.02 0.09
fatigue 0.32 0.50 0.75 0.11 0.24 0.36 0.13 0.25 0.38
nausea 0.34 0.48 0.65 -0.18 -0.01 0.09 0.02 0.16 0.30
anger 0.19 0.42 0.61 0.21 0.41 0.67 0.01 0.14 0.34
guilt -0.21 -0.11 0.07 0.62 0.80 0.91 -0.13 -0.05 0.04
embarrassment -0.27 -0.16 0.02 0.63 0.75 0.86 0.02 0.11 0.19
pride -0.03 0.16 0.28 0.59 0.69 0.87 -0.10 0.02 0.13
hurt_feelings -0.09 0.06 0.32 0.47 0.68 0.84 -0.10 0.03 0.14
sadness 0.03 0.20 0.41 0.48 0.65 0.81 -0.10 0.03 0.21
love 0.13 0.32 0.60 0.30 0.50 0.68 -0.29 -0.05 0.09
happiness 0.23 0.40 0.54 0.33 0.41 0.54 -0.09 0.01 0.20
figuring_out -0.05 0.04 0.17 -0.23 -0.10 0.03 0.55 0.74 0.85
choice -0.05 0.06 0.19 -0.11 0.09 0.24 0.55 0.72 0.91
memory -0.35 -0.17 0.06 -0.19 0.05 0.33 0.63 0.71 0.77
temperature -0.13 0.04 0.15 -0.17 -0.06 0.10 0.49 0.64 0.81
depth -0.13 0.03 0.26 -0.40 -0.04 0.23 0.34 0.55 0.70
awareness -0.04 0.10 0.32 0.00 0.11 0.29 0.38 0.52 0.63
Interfactor correlations and bootstrapped confidence intervals
lower estimate upper
fctr1-fctr2 0.368 0.49 0.56
fctr1-fctr3 0.193 0.36 0.40
fctr2-fctr3 0.031 0.29 0.48
Factor Analysis with confidence intervals using method = psych::fa(r = d_young_wide, nfactors = nfactors_young, n.iter = n_iter,
rotate = chosen_rot, cor = chosen_cor)
Factor Analysis using method = minres
Call: psych::fa(r = d_young_wide, nfactors = nfactors_young, n.iter = n_iter,
rotate = chosen_rot, cor = chosen_cor)
Standardized loadings (pattern matrix) based upon correlation matrix
factor1 factor2 factor3 h2 u2 com
anger 0.87 -0.11 -0.03 0.63 0.37 1.0
hunger 0.59 0.18 0.08 0.58 0.42 1.2
hurt_feelings 0.58 0.13 0.04 0.46 0.54 1.1
smell 0.55 0.12 0.01 0.40 0.60 1.1
fatigue 0.54 0.09 0.21 0.54 0.46 1.4
sadness 0.47 0.27 -0.06 0.40 0.60 1.6
nausea 0.46 0.28 0.00 0.44 0.56 1.7
pain 0.44 0.11 0.06 0.30 0.70 1.2
happiness 0.01 0.78 0.07 0.67 0.33 1.0
love -0.01 0.76 -0.02 0.56 0.44 1.0
pride 0.22 0.51 0.07 0.49 0.51 1.4
fear 0.27 0.36 0.09 0.37 0.63 2.0
embarrassment 0.21 0.31 0.14 0.31 0.69 2.3
temperature -0.12 0.06 0.77 0.55 0.45 1.1
memory 0.02 0.00 0.54 0.30 0.70 1.0
depth 0.15 -0.08 0.49 0.30 0.70 1.3
guilt 0.13 0.05 0.48 0.34 0.66 1.2
figuring_out 0.33 -0.14 0.45 0.37 0.63 2.0
choice 0.06 0.26 0.37 0.33 0.67 1.8
awareness 0.23 0.07 0.35 0.30 0.70 1.8
factor1 factor2 factor3
SS loadings 3.75 2.58 2.32
Proportion Var 0.19 0.13 0.12
Cumulative Var 0.19 0.32 0.43
Proportion Explained 0.43 0.30 0.27
Cumulative Proportion 0.43 0.73 1.00
With factor correlations of
factor1 factor2 factor3
factor1 1.00 0.58 0.50
factor2 0.58 1.00 0.46
factor3 0.50 0.46 1.00
Mean item complexity = 1.4
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.81 with Chi Square of 1017.87
The degrees of freedom for the model are 133 and the objective function was 1.69
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.06
The harmonic number of observations is 122 with the empirical chi square 129.66 with prob < 0.57
The total number of observations was 124 with Likelihood Chi Square = 192.34 with prob < 0.00059
Tucker Lewis Index of factoring reliability = 0.895
RMSEA index = 0.068 and the 90 % confidence intervals are 0.04 0.078
BIC = -448.76
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
factor1 factor2 factor3
Correlation of (regression) scores with factors 0.93 0.92 0.89
Multiple R square of scores with factors 0.87 0.84 0.79
Minimum correlation of possible factor scores 0.75 0.68 0.58
Coefficients and bootstrapped confidence intervals
low factor1 upper low factor2 upper low factor3 upper
anger 0.54 0.87 1.22 -0.55 -0.11 1.20 -0.27 -0.03 0.37
hunger 0.18 0.59 1.33 0.20 0.18 0.88 -0.17 0.08 0.40
hurt_feelings 0.43 0.58 1.09 -0.37 0.13 1.29 -0.24 0.04 0.38
smell 0.14 0.55 1.20 0.13 0.12 0.84 -0.20 0.01 0.27
fatigue 0.04 0.54 1.34 -0.19 0.09 1.04 -0.05 0.21 0.58
sadness 0.32 0.47 1.07 -0.08 0.27 1.12 -0.14 -0.06 0.17
nausea 0.02 0.46 1.32 0.05 0.28 1.11 -0.16 0.00 0.25
pain 0.17 0.44 0.81 -0.08 0.11 0.88 -0.11 0.06 0.40
happiness 0.05 0.01 1.01 0.41 0.78 1.20 -0.12 0.07 0.28
love 0.02 -0.01 0.86 0.44 0.76 1.17 -0.18 -0.02 0.30
pride 0.02 0.22 1.07 0.26 0.51 1.15 -0.11 0.07 0.34
fear -0.08 0.27 0.99 0.14 0.36 1.12 -0.13 0.09 0.36
embarrassment -0.07 0.21 0.88 0.02 0.31 0.95 -0.07 0.14 0.42
temperature -0.35 -0.12 0.43 -0.22 0.06 0.28 0.47 0.77 0.92
memory -0.32 0.02 0.46 -0.31 0.00 0.56 0.31 0.54 0.70
depth -0.16 0.15 0.58 -0.45 -0.08 0.52 0.20 0.49 0.82
guilt -0.12 0.13 0.67 -0.25 0.05 0.67 0.22 0.48 0.65
figuring_out 0.07 0.33 0.78 -0.49 -0.14 0.70 0.10 0.45 0.76
choice -0.11 0.06 0.68 0.11 0.26 0.56 0.08 0.37 0.63
awareness -0.03 0.23 0.73 -0.60 0.07 1.12 -0.02 0.35 0.79
Interfactor correlations and bootstrapped confidence intervals
lower estimate upper
fctr1-fctr2 0.41 0.58 0.63
fctr1-fctr3 0.17 0.50 0.52
fctr2-fctr3 0.20 0.46 0.42
Factor Analysis with confidence intervals using method = psych::fa(r = d_young_wide, nfactors = 2, n.iter = n_iter, rotate = chosen_rot,
cor = chosen_cor)
Factor Analysis using method = minres
Call: psych::fa(r = d_young_wide, nfactors = 2, n.iter = n_iter, rotate = chosen_rot,
cor = chosen_cor)
Standardized loadings (pattern matrix) based upon correlation matrix
factor1 factor2 h2 u2 com
hunger 0.72 0.05 0.57 0.43 1.0
nausea 0.70 -0.04 0.45 0.55 1.0
happiness 0.69 0.01 0.48 0.52 1.0
sadness 0.69 -0.10 0.40 0.60 1.0
pride 0.67 0.01 0.46 0.54 1.0
anger 0.67 0.00 0.45 0.55 1.0
love 0.66 -0.08 0.37 0.63 1.0
hurt_feelings 0.65 0.02 0.44 0.56 1.0
smell 0.63 -0.02 0.39 0.61 1.0
fear 0.58 0.04 0.37 0.63 1.0
fatigue 0.58 0.20 0.52 0.48 1.2
pain 0.51 0.04 0.29 0.71 1.0
embarrassment 0.48 0.10 0.30 0.70 1.1
temperature -0.07 0.76 0.52 0.48 1.0
memory 0.00 0.55 0.30 0.70 1.0
depth 0.05 0.52 0.30 0.70 1.0
guilt 0.15 0.48 0.34 0.66 1.2
figuring_out 0.17 0.46 0.34 0.66 1.3
awareness 0.26 0.36 0.31 0.69 1.8
choice 0.28 0.34 0.31 0.69 1.9
factor1 factor2
SS loadings 5.73 2.16
Proportion Var 0.29 0.11
Cumulative Var 0.29 0.39
Proportion Explained 0.73 0.27
Cumulative Proportion 0.73 1.00
With factor correlations of
factor1 factor2
factor1 1.0 0.6
factor2 0.6 1.0
Mean item complexity = 1.1
Test of the hypothesis that 2 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.81 with Chi Square of 1017.87
The degrees of freedom for the model are 151 and the objective function was 2.12
The root mean square of the residuals (RMSR) is 0.06
The df corrected root mean square of the residuals is 0.07
The harmonic number of observations is 122 with the empirical chi square 176.89 with prob < 0.074
The total number of observations was 124 with Likelihood Chi Square = 242.27 with prob < 3.5e-06
Tucker Lewis Index of factoring reliability = 0.859
RMSEA index = 0.077 and the 90 % confidence intervals are 0.053 0.086
BIC = -485.59
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
factor1 factor2
Correlation of (regression) scores with factors 0.95 0.89
Multiple R square of scores with factors 0.91 0.79
Minimum correlation of possible factor scores 0.82 0.58
Coefficients and bootstrapped confidence intervals
low factor1 upper low factor2 upper
hunger 0.62 0.72 0.96 -0.61 0.05 1.21
nausea 0.35 0.70 1.05 -0.41 -0.04 0.93
happiness 0.21 0.69 1.09 -0.29 0.01 0.92
sadness 0.45 0.69 0.92 -0.36 -0.10 0.59
pride 0.38 0.67 0.91 -0.36 0.01 1.09
anger 0.46 0.67 0.93 -0.54 0.00 0.96
love 0.18 0.66 1.03 -0.48 -0.08 0.84
hurt_feelings 0.34 0.65 0.97 -0.26 0.02 0.91
smell 0.46 0.63 0.85 -0.72 -0.02 1.00
fear 0.43 0.58 0.89 -0.51 0.04 0.94
fatigue 0.40 0.58 1.02 -0.29 0.20 1.10
pain 0.26 0.51 0.83 -0.41 0.04 1.07
embarrassment 0.22 0.48 0.86 -0.29 0.10 0.94
temperature -0.36 -0.07 0.86 0.68 0.76 0.93
memory -0.14 0.00 0.68 0.04 0.55 0.97
depth -0.24 0.05 0.80 0.19 0.52 0.85
guilt -0.01 0.15 0.73 0.11 0.48 0.93
figuring_out 0.03 0.17 0.79 -0.13 0.46 1.21
awareness -0.06 0.26 0.77 -0.06 0.36 1.11
choice 0.02 0.28 0.81 -0.01 0.34 0.97
Interfactor correlations and bootstrapped confidence intervals
lower estimate upper
fctr1-fctr2 0.35 0.6 0.66
Factor Analysis with confidence intervals using method = psych::fa(r = d_young_wide, nfactors = 1, n.iter = n_iter, rotate = chosen_rot,
cor = chosen_cor)
Factor Analysis using method = minres
Call: psych::fa(r = d_young_wide, nfactors = 1, n.iter = n_iter, rotate = chosen_rot,
cor = chosen_cor)
Standardized loadings (pattern matrix) based upon correlation matrix
factor1 h2 u2 com
hunger 0.74 0.55 0.45 1
fatigue 0.72 0.52 0.48 1
happiness 0.68 0.46 0.54 1
pride 0.66 0.44 0.56 1
anger 0.65 0.43 0.57 1
hurt_feelings 0.65 0.42 0.58 1
nausea 0.65 0.42 0.58 1
smell 0.60 0.36 0.64 1
fear 0.60 0.36 0.64 1
sadness 0.60 0.36 0.64 1
love 0.58 0.34 0.66 1
embarrassment 0.55 0.30 0.70 1
choice 0.53 0.28 0.72 1
pain 0.53 0.28 0.72 1
awareness 0.52 0.27 0.73 1
figuring_out 0.51 0.26 0.74 1
guilt 0.50 0.25 0.75 1
temperature 0.49 0.24 0.76 1
depth 0.43 0.19 0.81 1
memory 0.41 0.17 0.83 1
factor1
SS loadings 6.90
Proportion Var 0.35
Mean item complexity = 1
Test of the hypothesis that 1 factor is sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.81 with Chi Square of 1017.87
The degrees of freedom for the model are 170 and the objective function was 2.59
The root mean square of the residuals (RMSR) is 0.08
The df corrected root mean square of the residuals is 0.08
The harmonic number of observations is 122 with the empirical chi square 269.99 with prob < 1.6e-06
The total number of observations was 124 with Likelihood Chi Square = 297.95 with prob < 4.7e-09
Tucker Lewis Index of factoring reliability = 0.826
RMSEA index = 0.084 and the 90 % confidence intervals are 0.063 0.093
BIC = -521.5
Fit based upon off diagonal values = 0.95
Measures of factor score adequacy
factor1
Correlation of (regression) scores with factors 0.96
Multiple R square of scores with factors 0.92
Minimum correlation of possible factor scores 0.84
Coefficients and bootstrapped confidence intervals
low factor1 upper
hunger 0.68 0.74 0.84
fatigue 0.60 0.72 0.81
happiness 0.57 0.68 0.82
pride 0.55 0.66 0.78
anger 0.50 0.65 0.77
hurt_feelings 0.53 0.65 0.80
nausea 0.56 0.65 0.75
smell 0.45 0.60 0.80
fear 0.51 0.60 0.73
sadness 0.50 0.60 0.72
love 0.52 0.58 0.70
embarrassment 0.46 0.55 0.70
choice 0.43 0.53 0.63
pain 0.36 0.53 0.65
awareness 0.40 0.52 0.62
figuring_out 0.40 0.51 0.57
guilt 0.33 0.50 0.61
temperature 0.38 0.49 0.60
depth 0.25 0.43 0.55
memory 0.29 0.41 0.46
Linear mixed model fit by REML ['lmerMod']
Formula: diff ~ comparison * scale(age, scale = F) + (1 | subid)
Data: df_endorsements_diff
REML criterion at convergence: 2861.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.08026 -0.56230 0.02049 0.49809 2.77031
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.826 0.9088
Residual 2.713 1.6471
Number of obs: 702, groups: subid, 234
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.09402 0.08599 -1.093
comparisonBH_GM -0.38034 0.08792 -4.326
comparisonMB_GM -0.04701 0.08792 -0.535
scale(age, scale = F) 0.17740 0.04631 3.830
comparisonBH_GM:scale(age, scale = F) -0.23699 0.04735 -5.005
comparisonMB_GM:scale(age, scale = F) 0.08870 0.04735 1.873
Correlation of Fixed Effects:
(Intr) cBH_GM cMB_GM s(,s=F cBHs=F
cmprsnBH_GM 0.000
cmprsnMB_GM 0.000 -0.500
scl(g,sc=F) 0.000 0.000 0.000
cBH_GM:(s=F 0.000 0.000 0.000 0.000
cMB_GM:(s=F 0.000 0.000 0.000 0.000 -0.500
Call:
lm(formula = diff ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "BODY minus HEART"))
Residuals:
Min 1Q Median 3Q Max
-4.6524 -0.6719 0.3589 0.6677 4.6555
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.47436 0.11072 -4.284 2.69e-05 ***
scale(age, scale = F) -0.05959 0.05963 -0.999 0.319
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.694 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.004286, Adjusted R-squared: -6.251e-06
F-statistic: 0.9985 on 1 and 232 DF, p-value: 0.3187
Call:
lm(formula = diff ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "MIND minus BODY"))
Residuals:
Min 1Q Median 3Q Max
-4.4025 -1.3496 -0.1797 0.7691 5.5504
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.14103 0.12899 -1.093 0.275382
scale(age, scale = F) 0.26610 0.06947 3.830 0.000165 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.973 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.05948, Adjusted R-squared: 0.05543
F-statistic: 14.67 on 1 and 232 DF, p-value: 0.0001647
Call:
lm(formula = diff ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "MIND minus HEART"))
Residuals:
Min 1Q Median 3Q Max
-5.7817 -1.0942 -0.0243 1.2580 5.0960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.33333 0.12836 2.597 0.01 *
scale(age, scale = F) 0.32569 0.06913 4.711 4.24e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.963 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.08732, Adjusted R-squared: 0.08338
F-statistic: 22.2 on 1 and 232 DF, p-value: 4.244e-06
Joining, by = c("param", "Estimate", "Std..Error", "t.value", "Pr...t..", "comparison")
Joining, by = c("param", "Estimate", "Std..Error", "t.value", "Pr...t..", "comparison")
Linear mixed model fit by REML ['lmerMod']
Formula: diff_abs ~ comparison * scale(age, scale = F) + (1 | subid)
Data: df_endorsements_diff
REML criterion at convergence: 2378.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.59494 -0.60840 -0.09506 0.53532 2.75598
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.5669 0.7529
Residual 1.2679 1.1260
Number of obs: 702, groups: subid, 234
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.40456 0.06503 21.599
comparisonBH_GM -0.14387 0.06010 -2.394
comparisonMB_GM 0.06980 0.06010 1.161
scale(age, scale = F) 0.08716 0.03502 2.489
comparisonBH_GM:scale(age, scale = F) 0.02464 0.03237 0.761
comparisonMB_GM:scale(age, scale = F) -0.06013 0.03237 -1.858
Correlation of Fixed Effects:
(Intr) cBH_GM cMB_GM s(,s=F cBHs=F
cmprsnBH_GM 0.000
cmprsnMB_GM 0.000 -0.500
scl(g,sc=F) 0.000 0.000 0.000
cBH_GM:(s=F 0.000 0.000 0.000 0.000
cMB_GM:(s=F 0.000 0.000 0.000 0.000 -0.500
Call:
lm(formula = diff_abs ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "BODY minus HEART"))
Residuals:
Min 1Q Median 3Q Max
-1.6244 -1.0446 -0.1915 0.7121 3.9771
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.26068 0.07902 15.953 < 2e-16 ***
scale(age, scale = F) 0.11180 0.04256 2.627 0.00919 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.209 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.02888, Adjusted R-squared: 0.0247
F-statistic: 6.9 on 1 and 232 DF, p-value: 0.009193
Call:
lm(formula = diff_abs ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "MIND minus BODY"))
Residuals:
Min 1Q Median 3Q Max
-1.5618 -1.4221 -0.4329 0.5894 4.4495
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.47436 0.09163 16.091 <2e-16 ***
scale(age, scale = F) 0.02703 0.04935 0.548 0.584
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.402 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.001291, Adjusted R-squared: -0.003013
F-statistic: 0.3 on 1 and 232 DF, p-value: 0.5844
Call:
lm(formula = diff_abs ~ scale(age, scale = F), data = df_endorsements_diff %>%
filter(comparison == "MIND minus HEART"))
Residuals:
Min 1Q Median 3Q Max
-1.8730 -1.2235 -0.2740 0.7555 4.3065
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.47863 0.09425 15.688 <2e-16 ***
scale(age, scale = F) 0.12266 0.05076 2.416 0.0165 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.442 on 232 degrees of freedom
(13 observations deleted due to missingness)
Multiple R-squared: 0.02455, Adjusted R-squared: 0.02034
F-statistic: 5.838 on 1 and 232 DF, p-value: 0.01645
Joining, by = c("param", "Estimate", "Std..Error", "t.value", "Pr...t..", "comparison")
Joining, by = c("param", "Estimate", "Std..Error", "t.value", "Pr...t..", "comparison")
Joining, by = c("param", "Estimate", "Std..Error", "t.value", "Pr...t..", "comparison", "diff_type")
Ignoring unknown aesthetics: fill
[38;5;246m# A tibble: 3 x 6[39m
tercile n min max mean median
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m
[38;5;250m1[39m 1 79 4.00 5.27 4.70 4.68
[38;5;250m2[39m 2 79 5.27 7.94 6.62 6.72
[38;5;250m3[39m 3 78 7.94 9.99 8.92 8.84
Joining, by = c("capacity", "factor", "loading", "tercile")
Joining, by = c("capacity", "factor", "loading", "tercile")
Joining, by = "capacity"
Joining, by = "capacity"
Joining, by = "capacity"
Joining, by = "capacity"
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Using alpha for a discrete variable is not advised.